phamgialinhlx/negative-sentiment-26-02-2025

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 3 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
1	'Sind die Mahlzeiten für bestimmte Diäten geeignet?' 'Kann ich eine einzelne Mahlzeit anpassen?' 'Gibt es eine Möglichkeit, meine Bestellungen zu verfolgen?'
0	'Mein Essen war kalt und schmeckte nicht frisch.' 'Die Lieferung hat viel länger gedauert als angegeben.' 'Die Portionen sind viel zu klein für den Preis.'
2	'Die Portionen sind genau richtig und sättigend.' 'Die Mahlzeiten sind köstlich und perfekt gewürzt!' 'Ich bin sehr zufrieden mit der Qualität der Zutaten.'

Evaluation

Metrics

Label	Accuracy
all	0.4167

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("phamgialinhlx/negative-sentiment-26-02-2025")
# Run inference
preds = model("Es ist schön, verschiedene Essensoptionen zu haben.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	6.9583	10

Label	Training Sample Count
0	8
1	8
2	8

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (3, 3)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (2e-05, 2e-05)
• head_learning_rate: 2e-05
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0167	1	0.2407	-
0.8333	50	0.168	-
1.6667	100	0.0251	-
2.5	150	0.0018	-

Framework Versions

• Python: 3.11.11
• SetFit: 1.1.1
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}